{"id":"W2272231993","doi":"10.1063/1.4941709","title":"An automated microfluidic system for screening <i>Caenorhabditis elegans</i> behaviors using electrotaxis","year":2016,"lang":"en","type":"article","venue":"Biomicrofluidics","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs","keywords":"Caenorhabditis elegans; Microfluidics; Computer science; Biology; High-throughput screening; Genetic screen; Optogenetics; Chemical genetics; Computational biology; Neuroscience; Phenotype; Nanotechnology; Bioinformatics; Genetics; Gene; Materials science; Small molecule","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004030647,0.0004250729,0.0003504001,0.0001325683,0.0003486105,0.00009450762,0.0005359066,0.0004297535,0.000009469738],"category_scores_gemma":[0.00003819237,0.0003762391,0.0002196872,0.0001475545,0.0001816862,0.0000191331,0.0001054019,0.00007533046,0.00001376781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001160506,"about_ca_system_score_gemma":0.0001982583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001259361,"about_ca_topic_score_gemma":0.000008506559,"domain_scores_codex":[0.9975308,0.0001175789,0.0005168827,0.0008336727,0.0001995713,0.0008015067],"domain_scores_gemma":[0.9984469,0.00002021633,0.0001983452,0.0008164942,0.0002588303,0.0002592222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009512966,0.00008069377,0.001900885,0.00005161676,0.00007363887,0.000005179446,0.00005701309,0.000003062904,0.9857154,0.00003103783,0.01004399,0.001942289],"study_design_scores_gemma":[0.001416702,0.000558992,0.0003626825,0.00006786196,0.0001236499,0.000118964,0.00007937742,0.0003339562,0.9658056,0.000008073701,0.03055372,0.0005704126],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.731141,0.003221401,0.2640074,0.00006915013,0.0004687295,0.0005118995,0.0003184901,0.0002512027,0.00001075283],"genre_scores_gemma":[0.9877793,0.000756102,0.009854934,0.0002144976,0.0007578787,0.00005013654,0.000278246,0.0001596397,0.0001492871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2566383,"threshold_uncertainty_score":0.9998689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383632528984908,"score_gpt":0.2716318545115843,"score_spread":0.2577955292217353,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}